ArticleCognition2022
A neural network model of the effect of prior experience with regularities on subsequent category learning.
Article in Cognition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning.NPJ science of learning · 2023Article
- Long-term priors constrain category learning in the context of short-term statistical regularities.Psychonomic bulletin & review · 2022Article
- Naturalistic speech supports distributional learning across contexts.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- A neural network model of the effect of prior experience with regularities on subsequent category learning.Cognition · 2022Article
- When unsupervised training benefits category learning.Cognition · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Categories are often structured by the similarities of instances within the category defined across dimensions or features. Researchers typically assume that there is a direct, linear relationship between the physical input dimensions across which category exemplars are defined and the psychological representation of these dimensions. However, this assumption is not always warranted. Through a set of simulations, we demonstrate that the psychological representations of input dimensions developed through long-term prior experience can place very strong constraints on category learning. We compare the model's behavior to auditory, visual, and cross-modal human category learning and make conclusions regarding the nature of the psychological representations of the dimensions in those studies. These simulations support the conclusion that the nature of psychological representations of input dimensions is a critical aspect to understanding the mechanisms underlying category learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.